Remaining Useful Life Prediction Using Time-Frequency Feature and Multiple Recurrent Neural Networks

Abstract

This paper presents a new framework to improve processing time and accuracy for remaining useful life (RUL) prediction of a degrading equipment. Most of the existing machine learning-based RUL prediction approaches are often not robust to noise and outliers, requiring a large overhead processing to select best features and a complex procedure to build an accurate model. This paper explores using signal decomposition methods to eliminate the noise, and multiple deep learning techniques to minimize the outliers for a fast and accurate RUL prediction system. Specifically, we leverage either the wavelet transform (WT) or empirical mode decomposition (EMD) to extract time-frequency domain features, then build an ensemble deep neural network model that jointly utilize three different recurrent neural networks (RNNs) to predict the RUL of a degrading equipment. We evaluate the our proposed approach using aircraft gas turbine engine data from NASA, to show the effects of WT and EMD on the RUL prediction performance in terms of processing time and RUL accuracy. Experimental results demonstrate that our proposed approach significantly outperforms existing approaches.